A sparsity-enforcing method for learning face features

IEEE Transactions on Image Processing

Volumn 18, Issue 1, 2009, Pages 188-201

  Destrero, Augusto ^a   De Mol, Christine ^b   Odone, Francesca ^a   Verri, Alessandro ^a  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

Author keywords

Face features; Feature selection; Regularization methods; Sparsity enforcing penalty

Indexed keywords

COMPETITION; COMPUTATIONAL EFFICIENCY; COMPUTER VISION; DYNAMIC LOADS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE PROCESSING; LAW ENFORCEMENT; LINEAR SYSTEMS; OPTIMIZATION;

CASE STUDIES; EYES DETECTIONS; FACE FEATURES; FEATURE SELECTION; FULL OPTIMIZATIONS; IMAGE MEASUREMENTS; ITERATIVE THRESHOLDING; MEMORY SAVINGS; OBJECT DETECTIONS; REGULARIZATION METHODS; SELECTION PROCEDURES; SIZE OPTIMIZATIONS; SMALL TRAININGS; SPARSE SOLUTIONS; SPARSITY-ENFORCING PENALTY; SYSTEMS OF EQUATIONS; TRAINABLE SYSTEMS;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMETRY; COMPUTER ASSISTED DIAGNOSIS; FACE; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; FACE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 58049206648     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2008.2007610     Document Type: Article

References (38)

1. Y. Amit and D. Geman, "A computational model for visual selection," Neural Comput., vol. 11, pp. 1691-1715, 1999.
2. P. J. Burt, "Smart sensing within a pyramidal vision machine," Proc. IEEE, vol. 76, no. 8, pp. 1006-1015, Oct. 1988.
3. S. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol. 20, no. 1, 1998.
4. I. Daubechies, M. Defrise, and C. De Mol, "An iterative thresholding algorithm for linear inverse problems with a sparsity constraint," Commun. Pure Appl. Math., vol. 57, pp. 1416-1457, 2004.
5. A. Destrero, C. De Mol, F. Odone, and A. Verri, Y. Yagi, Ed. et al. "A regularized approach to feature selection for face detection," in Proc. Asian Conf. Computer Vision, ACCV, 2007, vol. 4844, LNCS, pp. 881-890.
6. F. Fleuret and D. Geman, "Coarse-to-fine face detection," Int. J. Comput. Vis., vol. 41, pp. 85-107, 2001.
7. J. Fowler, "The redundant discrete wavelet transform and additive noise," IEEE Signal Process. Lett., vol. 12, no. 9, pp. 639-632, Sep. 2005.
8. E. Franceschi, F. Odone, F. Smeraldi, and A. Verri, "Feature selection with non-parametric statistics," presented at the IEEE Int. Conf. Image Processing, 2005.
9. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," in Proc. Eur. Conf. Computational Learning Theory, 1995, pp. 23-37.
10. J. Friedman, T. Hastie, and R. Tibshirani, Additive Logistic Regression: A Statistical View of Boosting, 1998.
11. I. Guyon and E. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
12. B. Heisele, T. Serre, S. Mukherjee, and T. Poggio, "Feature reduction and hierarchy of classifiers for fast object detection in video images," presented at the IEEE CVPR, 2001.
13. B. Heisele, T. Serre, M. Pontil, and T. Poggio, "Component-based face detection," presented at the CVPR, 2001.
14. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
15. E. L. Lehmann, Nonparametrics: Statistical Methods Based on Ranks. New York: Holden-Day, 1975.
16. S. Z. Li and Z. Zhang, "FloatBoost learning and statistical face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, Sep. 2004.
17. S. Mann and S. Haykin, "The chirplet transform: A generalization of Gabor's logon transform," in Proc. Vision Interface, 1991, pp. 205-212.
18. A. Mohan, C. Papageorgiou, and T. Poggio, "Example-based object detection in images by components," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 4, pp. 349-361, Apr. 2001.
19. B. A. Olshauser and D. J. Field, "Sparse coding with an overcomplete basis set: A strategy employed by V1?," Vis. Res., vol. 37, no. 23, pp. 3311-3325, 1997.
20. E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: An application to face detection," presented at the CVPR, 1997.
21. C. Papageorgiou and T. Poggio, "A trainable system for object detection," Int. J. Comput. Vis., vol. 38, no. 1, pp. 15-33, 2000.
22. M. Pontil and A. Verri, "Support vector machines for 3-d object recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 637-646, Jun. 1998.
23. N. Rajpoot, R. G. Wilson, F. G. Meyer, and R. Coifman, "A new basis selection paradigm for wavelet packet image coding," presented at the IEEE Int. Conf. Image Processing, 2001.
24. A. Resefield and G. J. Vanderbrug, "Coarse-fine template matching," IEEE Trans. Syst. Man. Cybern., vol. 7, no. 2, pp. 104-107, Feb. 1977.
25. D. Roth, M. Yang, and N. Ahuja, "A snowbased face detector," Neur. Inf. Process., vol. 12, 2000.
26. H. Schneiderman and T. Kanade, "A statistical method for 3D object detection applied to faces and cars," presented at the Int. Conf. Computer Vision, 2000.
27. F. Smeraldi, "Ranklets: Orientation selective non-parametric features applied to face detection," in Proc. Int. Conf. Pattern Recognition 2002, vol. 3, pp. 379-382.
28. K. Sung and T. Poggio, "Example-based learning for view-based human face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 39-51, Jan. 1998.
29. R. Tibshirani, "Regression shrinkage and selection via the lasso," J Roy. Statist. Soc. B, vol. 58, no. 1, pp. 267-288, 1996.
30. J. K. Tsotsos, S. M. Culhane, W. Y. Wai, Y. H. Lai, N. Davis, and F. Nuflo, "Modeling visual attention via selective tuning," Artif Intell. J., vol. 78, no. 1-2, 1995.
31. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
32. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
33. P. Viola and M. J. Jones, "Robust real-time face detection," Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, 2004.
34. Q. Wang, J. Yang, and W. Yang, "Face detection using rectangle features and SVM," Int. J. Intell. Technol., vol. 1, no. 3, 2006.
35. J. Weston, A. Elisseeff, B. Scholkopf, and M. Tipping, "The use of zero-norm with linear models and kernel methods," J. Mach. Learn. Res., vol. 3, 2003.
36. M.-H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting faces in images: A survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 1, pp. 34-58, Jan. 2002.
37. D. Zhang, S. Z. Li, and D. Gatica-Perez, "Real-time face detection using boosting in hierarchical feature spaces," presented at the ICPR, 2004.
38. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, "1-norm support vector machines," in Advances in Neural Information Processing SYstems. Cambridge, MA: MIT Press, 2004, vol. 16.